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This introductory guide provides a thorough explanation of the mathematics and algorithms used in standard data analysis techniques within systems biology, biochemistry, and biophysics. Each part of the book covers the mathematical background and practical applications of a given technique. Readers will gain an understanding of the mathematical and algorithmic steps needed to use these software tools appropriately and effectively, as well how to assess their specific circumstance and choose the optimal method and technology. Ideal for students planning for a career in research, early-career…mehr
This introductory guide provides a thorough explanation of the mathematics and algorithms used in standard data analysis techniques within systems biology, biochemistry, and biophysics. Each part of the book covers the mathematical background and practical applications of a given technique. Readers will gain an understanding of the mathematical and algorithmic steps needed to use these software tools appropriately and effectively, as well how to assess their specific circumstance and choose the optimal method and technology. Ideal for students planning for a career in research, early-career researchers, and established scientists undertaking interdisciplinary research.
Paola Lecca, Assistant Professor, Faculty of Engineering, Free University of Bozen-Bolzano, Italy Bruno Carpentieri, Associate Professor, Faculty of Engineering, Free University of Bozen-Balzano, Italy
Inhaltsangabe
1. Introduction to graph theory.- 2. Biological networks.- 3. Network inference for drug discovery- 4. Introduction to differential and integral calculus.- 5. Modelling chemical reactions.- 6. Reaction-diffusion systems.- 7. Linear algebra background.- 8. Regression.- 9. Cardiac electrophysiology.-
1. Introduction to graph theory.- 2. Biological networks.- 3. Network inference for drug discovery- 4. Introduction to differential and integral calculus.- 5. Modelling chemical reactions.- 6. Reaction-diffusion systems.- 7. Linear algebra background.- 8. Regression.- 9. Cardiac electrophysiology.-
1. Introduction to graph theory.- 2. Biological networks.- 3. Network inference for drug discovery- 4. Introduction to differential and integral calculus.- 5. Modelling chemical reactions.- 6. Reaction-diffusion systems.- 7. Linear algebra background.- 8. Regression.- 9. Cardiac electrophysiology.-
1. Introduction to graph theory.- 2. Biological networks.- 3. Network inference for drug discovery- 4. Introduction to differential and integral calculus.- 5. Modelling chemical reactions.- 6. Reaction-diffusion systems.- 7. Linear algebra background.- 8. Regression.- 9. Cardiac electrophysiology.-
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